Vehicle re-identification using relative entropy representation and gated recurrent unit networks

Abstract

Abstract Vehicle Re-Identification is a prevailing subject in the field of computer vision technologies. Major problem in vehicle re-id is re identifying the vehicle from different viewpoints with occlusion and low illumination. In this work, we have proposed a deep-learning framework which effectively processes the latent representation of the vehicle image instances. The key to our framework is GRUs, which is used to determine the relationship between the vehicle image instances over a period of time, without compromising the computational cost, which is also used to encode the data.